code: def test_calculate_all_metafeatures(sparse_data):
      X, y, feat_type = sparse_data
      mf = meta_features.calculate_all_metafeatures(
          X, y, feat_type, "2", logger=logging.getLogger("Meta")
      )
      assert 52 == len(mf.metafeature_values)
  
imports: import logging
  import os
  import arff
  import numpy
  from scipy import sparse
  from sklearn.impute import SimpleImputer
  from sklearn.preprocessing import StandardScaler
  import autosklearn.metalearning.metafeatures.metafeatures
  from autosklearn.pipeline.components.data_preprocessing.feature_type import FeatTypeSplit
  import pytest
label_desc: Use image node_id 20 for calling a global function with example usage: test_calculate_all_metafeatures(sparse_data) without return types